Ursula Cottone is Chief Data and Architecture Officer at Huntington Bank, where she leads the enterprise data and architecture strategy with a focus on driving business value through disciplined governance, modern platforms, and cultural transformation.
Her career has consistently operated at the intersection of business strategy, technology, and data. She began in operational and analytical roles before moving into financial services, spending 17 years at KeyBank across consulting, strategy, shared services, and ultimately serving as Chief Data Officer. Those experiences shaped her conviction that data must be governed, high quality, and tightly aligned to business outcomes and not treated as a technical afterthought.
As Chief Data Officer at Citizens Bank, Ursula built enterprise data capabilities spanning master data, metadata, stewardship, and the organization’s first enterprise data lake. That work reinforced the importance of enterprise alignment and disciplined data supply chain management in regulated environments.
Since joining Huntington in 2018, she has led the evolution of a modern, cloud-native data ecosystem, established an MLOps foundation, and refreshed the bank’s data strategy around five core priorities: fostering a data-driven culture, accelerating adoption of modern architecture, mitigating data risk, delivering high-quality operations, and building a best-in-class team.
Her leadership philosophy reflects a balance of strategic alignment, governance rigor, and people-first change management, recognizing that sustainable impact in data and AI comes as much from trust and adoption as from technology itself.
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
“Effective data and AI leadership requires a blend of strategic clarity, governance rigor, cross functional influence, and change leadership. Leaders must translate business strategy into data capabilities, making complex topics actionable and outcome oriented. They must build systems that scale: clear policies, repeatable processes, strong quality management, and architectures that enable safe innovation.
“The traits most impactful in my organization are:
- Outcome focus: tying every initiative to measurable business value.
- Cross functional alignment: creating shared accountability between business segments, risk, and technology.
- Operational discipline: using clear roles, decision rights, and standards to reduce friction and enable safe scaling.
- Culture building: improving data literacy, transparency, and trust.
“These traits matter because our data strategy depends on governance and enablement working together. Without strong partnerships, data risk management, or a culture of accountability, even the best technology will not drive business value.”
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?
“My biggest piece of non‑traditional advice is this: prioritize collaboration as deliberately as you prioritize data strategy. Technical excellence is essential, but your real influence comes from how well you listen, build relationships, and create a coalition around the work.
“Early in my time at Huntington, I met with more than 150 colleagues to understand their priorities, language, and pain points. That investment (deep listening, not technical execution) was what enabled me to build a data strategy people believed in and were willing to champion. Collaboration isn’t a soft skill; it is the skill that determines whether your strategy survives first contact with the organization.
“Build your own internal company mini board of directors, nurture advocates across risk, finance, technology, and operations, and keep communication flowing at all levels. When people feel heard and included, they don’t just support your strategy; they help you deliver it.”